DBMS > EsgynDB vs. openGemini vs. Oracle Coherence vs. Spark SQL

System Properties Comparison EsgynDB vs. openGemini vs. Oracle Coherence vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Oracles in-memory data grid solution

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.09
Rank	#361	Overall
	#37	Time Series DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.esgyn.cn

www.opengemini.org
github.com/openGemini

www.oracle.com/java/coherence

spark.apache.org/sql

Technical documentation

docs.opengemini.org/guide

docs.oracle.com/en/middleware/standalone/coherence

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Esgyn

Huawei and openGemini community

Oracle

Apache Software Foundation

Initial release

2015

2022

2007

2014

Current release

1.1, July 2023

14.1, August 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

Java

Scala

Server operating systems

Linux

Linux
Windows

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP REST

JCache
JPA
RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

.Net
C++
Java

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Administrators and common users accounts

authentification to access the cache via certificates or http basic authentication

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